Predicting transportation supply (that is, the availability of buses, taxis, etc.) and transportation demand (that is, the number of potential passengers or travelers) in a real-world scenario is a complex and challenging task. Traffic information systems provide information about current traffic situations and what commuters or travelers can potentially do in hopes of accomplishing their travel needs. Such existing approaches include applying sensors (such as global positioning system (GPS) sensors) to vehicles. Additionally, users can provide information such as traffic counts and speed, location of vehicles and users, etc. However, such information is often incomplete, as it does not provide additional context such as latent traffic demand and supply, the number of commuters likely to take a particular route, the number of vehicles that effectively become available at a location for a destination at a particular time of day, etc. Accordingly, a need exists for the implementation of techniques that optimize resources to more accurately predict traffic events and conditions.